Natural resource investigation system and method based on GIS

Abstract

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of natural resource survey, and in particular to a natural resource survey system and method based on GIS. Background Art

[0002] Natural resource surveys are the foundation of resource development, environmental protection and sustainable development strategies, and can provide decision makers with accurate key information such as resource distribution, environmental conditions and the impact of human activities. At present, with the rapid development of remote sensing technology, GIS (geographic information system), big data analysis and intelligent algorithms, more and more technologies are being applied to the survey and management of natural resources.

[0003] Although existing technologies have made certain progress in the field of natural resource surveys, there are still several technical problems and challenges. First, in terms of data collection, existing technologies mainly rely on equipment such as remote sensing satellites and ground sensors. Although these devices can obtain a large amount of data, they face the problems of insufficient timeliness and spatial resolution of data acquisition, especially in complex geographical environments. The accuracy and real-time nature of the data often cannot meet actual needs. Secondly, in the data processing and analysis stage, due to the diverse sources of collected data and uneven quality, the existing cleaning, denoising and standardization methods often cannot completely remove invalid data and outliers, resulting in reduced accuracy of subsequent analysis results. In addition, the existing GIS platform and data integration technology have not yet achieved efficient fusion and spatial calibration of multi-source heterogeneous data, and cannot give full play to the advantages of different data sources, resulting in low accuracy and consistency in spatial data analysis. Finally, most of the current resource utilization assessment and environmental impact analysis methods focus on a single resource or environmental indicator, lack comprehensive analysis of multiple factors and optimization decision support, and cannot achieve accurate assessment of different regions and resources and the generation of optimal utilization plans. Summary of the invention

[0004] The present invention provides a natural resource survey system and method based on GIS.

[0005] A natural resource survey method based on GIS includes the following steps: S1, data collection: by deploying a variety of sensors, remote sensing satellites and drone equipment, real-time collection of environmental data, human activity data and resource distribution data in the natural resource survey area; S2, data preprocessing: cleaning and denoising the collected environmental data, human activity data, and resource distribution data, removing invalid data and outliers, and performing standardization; S3, spatial data integration: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; S4, dynamic monitoring and analysis: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change laws of resources and generate dynamic monitoring data; S5, resource utilization assessment: Based on dynamic monitoring data, combined with ecological environmental impact assessment models and resource utilization models, analyze the sustainable utilization status of different regions and different resources, and predict the trend of resource consumption and environmental impact; S6, decision support: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

[0006] Optionally, the S1 includes: S11, environmental data collection: by deploying a variety of sensors and equipment, real-time environmental data is collected in the natural resources survey area, and the environmental data includes meteorological data, soil moisture and vegetation coverage.

[0007] S12, human activity data collection: by deploying a variety of monitoring equipment, real-time collection of human activity data in the natural resource survey area, the human activity data including land use type, pollution source distribution and traffic flow; S13, resource distribution data collection: by deploying remote sensing satellites and unmanned aerial vehicle equipment, real-time collection of natural resource distribution data, the resource distribution data includes water resources, mineral resources and forest resources.

[0008] Optionally, S2 includes: S21, data cleaning: data cleaning of collected environmental data, human activity data and resource distribution data; S22, data denoising: denoising the cleaned environmental data, human activity data and resource distribution data; S23, data standardization processing: standardize the denoised environmental data, human activity data and resource distribution data.

[0009] Optionally, the S3 includes: S31, spatial calibration of environmental data: matching environmental data (such as meteorological data, soil moisture, vegetation coverage, etc.) with geographic coordinate systems (such as WGS-84 coordinate system); S32, spatial calibration of human activity data: aligning human activity data (such as land use type, pollution source distribution, traffic flow, etc.) with geographic coordinates; S33, spatial calibration of resource distribution data: calibrate the spatial coordinates of resource distribution data (such as water resources, mineral resources, forest resources, etc.); S34, Data integration based on GIS platform: Use GIS platform (such as ArcGIS, QGIS, etc.) to spatially integrate data from different sources; S35, regional map information superposition: superimposing regional map information (such as administrative division map, road map, river map, etc.) with environmental data, human activity data and resource distribution data; S36, data set generation: through S31 to S35, a geographic information data set is formed, and the geographic information data set includes environmental data, human activity data, resource distribution data and regional map information.

[0010] Optionally, the S4 includes: S41, Dynamic monitoring system architecture design: Build a dynamic monitoring system based on the GIS platform. The dynamic monitoring system obtains environmental data, human activity data and resource distribution data in real time, and combines geographic information data sets to monitor real-time resources and environmental factors; S42, monitoring area delineation: In the GIS platform, the boundaries of the monitoring area are delineated according to the actual survey area, and different monitoring areas are divided according to resource types and human activity density; S43, real-time update of dynamic monitoring data: Based on the real-time collected environmental data, human activity data and resource distribution data, the dynamic monitoring system updates the monitoring data in real time and converts it into a visual display form.

[0011] Optionally, S4 further includes: S44, construction of spatiotemporal analysis model: Based on GIS technology, a spatiotemporal analysis model is constructed to analyze the spatiotemporal relationship between resource changes and environmental factors. The spatiotemporal analysis model is combined with a spatial autoregressive algorithm to predict future resource change trends; S45, Mining spatiotemporal change patterns: Use spatiotemporal change analysis methods to identify the dynamic change patterns of resource distribution and environmental factors.

[0012] S46, Dynamic Change Prediction: Based on historical data and real-time monitoring data, the future change trend of resources and environmental factors is predicted through regression analysis; S47, dynamic monitoring data generation: Combined with the results of spatiotemporal change analysis, dynamic monitoring data is generated. The dynamic monitoring data includes the spatiotemporal distribution information of resources and the changes in environmental factors.

[0013] Optionally, the S5 includes: S51, model input data selection and processing: select key factors affecting the ecological environment based on dynamic monitoring data; S52, selection of environmental impact factors: according to actual needs and regional characteristics, different environmental impact factors (such as water quality, air quality, soil quality, etc.) are selected as evaluation variables, and dynamic monitoring data are combined with environmental factors to establish an ecological environmental impact assessment model; S53, Ecological and environmental impact assessment model formulation: Based on the ecological and environmental impact assessment model, establish the relationship between various environmental factors and ecological and environmental impacts; S54, Grey correlation analysis method: When conducting ecological environmental impact assessment, the grey correlation analysis method is used to quantify the relationship between various environmental factors and ecological environmental impact; S55, generation of assessment results: based on the calculation results of grey correlation analysis, output the ecological environmental impact assessment results of different regions and generate environmental impact grades.

[0014] Optionally, the resource utilization model includes a resource consumption sub-model and an environmental impact prediction sub-model, specifically including: S56, resource consumption sub-model construction: Based on dynamic monitoring data, collect and integrate the utilization data of different resources (such as water resources, mineral resources, forest resources, etc.), including the amount of resource extraction, use and recycling, and combine environmental factors (such as climate change, land use, etc.) to establish a resource consumption sub-model to predict the resource consumption trend in different regions; S57, Construction of environmental impact prediction sub-model: Based on the resource consumption sub-model and environmental factor data, an environmental impact prediction sub-model is established to predict the potential impact of resource consumption on the ecological environment under different resource utilization conditions; S58, prediction of sustainable resource utilization: Based on the resource consumption sub-model and the environmental impact prediction sub-model, predict the sustainable utilization status of different regions and different resources, and evaluate the sustainability of resources in a certain period of time in the future.

[0015] Optionally, the S6 includes: S61, Generate optimization plan based on the results of ecological and environmental impact assessment: According to the assessment results of the ecological and environmental impact assessment model, analyze the environmental change trends in different regions and the potential impact of resource utilization on the environment, and generate natural resource optimization utilization plans for different regions.

[0016] S62, generating environmental protection measures based on the prediction results of the resource utilization model: according to the prediction results of the resource utilization model, analyzing the sustainable utilization status and consumption trends of different resources, and generating corresponding environmental protection measures.

[0017] S63, comprehensive generation of optimized utilization plans and environmental protection measures: Integrate the optimization plans and protection measures in S61 and S62 to form a comprehensive natural resource utilization and environmental protection optimization plan.

[0018] A GIS-based natural resource survey system is used to implement the above-mentioned GIS-based natural resource survey method, including the following modules: Data collection module: By deploying a variety of sensors, remote sensing satellites and drone equipment, it collects environmental data, human activity data and resource distribution data in the natural resource survey area in real time; Data preprocessing module: cleans and denoises the collected environmental data, human activity data, and resource distribution data, removes invalid data and outliers, and performs standardization processing; Spatial data integration module: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; Dynamic monitoring and analysis module: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change rules of resources and generate dynamic monitoring data; Resource utilization assessment module: Based on dynamic monitoring data, combined with the ecological environmental impact assessment model and resource utilization model, it analyzes the sustainable utilization status of different regions and different resources, and predicts the trend of resource consumption and environmental impact; Decision support module: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

[0019] Beneficial effects of the present invention: The present invention can effectively remove invalid and abnormal data through denoising, cleaning and standardization of collected data, ensure data quality, and lay a reliable foundation for subsequent analysis. Spatial data integration uses the GIS platform to calibrate data from different sources according to spatial coordinates, and combines regional map information to form a geographic information data set, thereby realizing the fusion of multi-source heterogeneous data. This data integration process can ensure the consistency and comparability between data, effectively support spatial analysis and dynamic monitoring, and through the integration and calibration of spatial data, it can achieve accurate monitoring and analysis of large areas.

[0020] The present invention, based on the results of the ecological environmental impact assessment model and the resource utilization model, the system can generate a comprehensive resource optimization utilization plan and environmental protection measures. Through model-based prediction, the system can effectively evaluate resource consumption trends and environmental impacts, and on this basis, propose the most optimized resource allocation plan. This decision support system can not only provide scientific resource management strategies, but also adjust and optimize according to the feedback of actual implementation to ensure that the implementation effect of the plan continues to achieve the expected goals. In particular, in terms of the balance between resource consumption and environmental protection, the system can provide targeted and sustainable solutions through multi-objective optimization algorithms, helping managers find the optimal balance between resource development and environmental protection, and ultimately achieving sustainable utilization of natural resources and long-term protection of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A schematic diagram of a method according to an embodiment of the present invention; Figure 2 Schematic diagram of a system flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0024] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0025] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0026] like Figure 1 As shown, a natural resource survey method based on GIS includes the following steps: S1, data collection: by deploying a variety of sensors, remote sensing satellites and drone equipment, real-time collection of environmental data, human activity data and resource distribution data in the natural resource survey area; S2, data preprocessing: clean and denoise the collected environmental data, human activity data, and resource distribution data, remove invalid data and outliers, and standardize them to conform to a unified format and scale, providing a reliable data basis for subsequent analysis; S3, spatial data integration: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; S4, dynamic monitoring and analysis: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change laws of resources and generate dynamic monitoring data; S5, resource utilization assessment: Based on dynamic monitoring data, combined with ecological environmental impact assessment models and resource utilization models, analyze the sustainable utilization status of different regions and different resources, and predict the trend of resource consumption and environmental impact; S6, decision support: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

[0027] S1 includes: S11, Environmental data collection: By deploying a variety of sensors and equipment, real-time environmental data is collected in the natural resource survey area. Environmental data includes meteorological data, soil moisture and vegetation coverage, including: Meteorological data collection: Real-time collection of meteorological parameters such as temperature, relative humidity, wind speed, precipitation, etc. through weather stations, temperature and humidity sensors, wind speed sensors and other equipment; Soil moisture collection: Through soil moisture sensors and soil temperature sensors, the moisture content in the soil is monitored in real time to ensure the accuracy and timeliness of the data; Vegetation coverage collection: Use remote sensing satellites or drones equipped with high-resolution image sensors to obtain vegetation coverage information in the area through image analysis.

[0028] S12, Human activity data collection: By deploying a variety of monitoring equipment, real-time human activity data in the natural resource survey area is collected. Human activity data includes land use type, pollution source distribution and traffic flow, including: Land use type collection: Use remote sensing satellites, drones or ground sensors to obtain land use information, and obtain land use type data such as cultivated land, cities, forests, wetlands, etc. in the region through classification and analysis of remote sensing images; Pollution source distribution collection: Through pollution source monitoring sensors, gas sensors (such as sulfur dioxide and nitrogen oxide sensors), and water quality monitoring equipment (such as dissolved oxygen sensors, ammonia nitrogen sensors, etc.), real-time collection of pollution source distribution information in the area, including data on air pollutants, water pollutants, soil pollution sources, etc.; Traffic flow collection: By installing traffic flow monitoring sensors or using drones, satellite images and other means, traffic flow data can be monitored in real time to obtain information such as vehicle flow and traffic density on the road; S13, Resource distribution data collection: By deploying remote sensing satellites and drone equipment, real-time natural resource distribution data is collected. Resource distribution data includes water resources, mineral resources and forest resources, including: Water resource distribution collection: Real-time monitoring of water distribution, lakes, rivers, reservoirs and other areas’ water resources through remote sensing satellite images, drones and ground hydrological stations; Mineral resource distribution collection: Use remote sensing technology, ground survey equipment and unmanned aerial vehicle-mounted mineral detection instruments to collect spatial distribution data of mineral resources, including mining areas, mineral reserves and other information; Forest resource distribution collection: Through remote sensing satellite images, drones and LiDAR technology, the distribution of forest resources is obtained in real time, including forest coverage, forest area, forest biomass and other data.

[0029] S2 includes: S21, data cleaning: Data cleaning of collected environmental data, human activity data and resource distribution data, including: S211, remove invalid data: Use integrity check rules to remove invalid data caused by missing fields, format failure or device failure in the collected data, expressed as: ; in, For valid data, is the original data, is invalid data; S212, processing duplicate data: merging the repeatedly collected data, using a deduplication algorithm to identify identical data records based on timestamps and spatial coordinates, and retaining only the latest or average value; S22, data denoising: denoising the cleaned environmental data, human activity data and resource distribution data, including: S221, random noise filtering: Smooth the collected data to remove random noise. Use the mean filter algorithm, the formula is: ; in, is the denoised data, is the size of the filter window, The first data points; S222, outlier detection: Identify outliers through statistical analysis methods (such as the 3-times standard deviation method) and remove or correct them. The formula is as follows: ; in, is the data value, is the data mean, is the standard deviation; S23, data standardization processing: standardize the denoised environmental data, human activity data and resource distribution data, including: S231, range normalization: Normalize the data to a uniform range (such as [0,1]) to eliminate the influence of different dimensions. The formula is as follows: ; in, is the standardized data, and are the minimum and maximum values ​​in the data set, respectively; S232, Data format unification: Convert data from different sources into a unified data format, including timestamp formatting, coordinate system conversion (such as unification to WGS-84 coordinate system), etc., to ensure data consistency.

[0030] S3 includes: S31, spatial calibration of environmental data: matching environmental data (such as meteorological data, soil moisture, vegetation coverage, etc.) with geographic coordinate systems (such as WGS-84 coordinate system). Specific methods include: The environmental data collected by the sensor is combined with the sensor's geographical location and converted into values ​​with spatial coordinates through spatial interpolation algorithms (such as Kriging interpolation and inverse distance weighted method). For remote sensing images or data captured by drones, use ground control points (GCPs) to perform image registration to ensure that the images are aligned with real geospatial coordinates; The output spatial data can be represented as a point, line, and surface data set with geographic information, including its spatial location, time mark, and related environmental attributes; S32, spatial calibration of human activity data: aligning human activity data (such as land use type, pollution source distribution, traffic flow, etc.) with geographic coordinates. Specific methods include: Use GIS platform to map data such as land use type and pollution source distribution into map coordinate system through spatial analysis tools (such as spatial interpolation and buffer analysis) to form vector or raster data with spatial attributes; For traffic flow data, each traffic flow record is associated with the corresponding geographical location through spatial matching of road network data and traffic monitoring points to ensure spatial consistency of the data; S33, spatial calibration of resource distribution data: spatial coordinate calibration of resource distribution data (such as water resources, mineral resources, forest resources, etc.), including: Use remote sensing images or drone images, ground survey data and other information to match resource distribution (such as water bodies, mining areas, forests, etc.) with actual geographic coordinates; Use image processing techniques (such as image classification and feature extraction) to analyze remote sensing images, identify different resource types, and calibrate their locations into a map coordinate system; The output result is a resource distribution layer with spatial information, including the distribution of water resources, the spatial distribution of forest coverage, etc. S34, Data integration based on GIS platform: Use GIS platform (such as ArcGIS, QGIS, etc.) to spatially integrate data from different sources. The specific steps include: Import the calibrated environmental data, human activity data, and resource distribution data into the GIS platform to ensure spatial alignment of different data layers; Use data fusion techniques (such as data resampling and spatial interpolation) to uniformly process data to ensure spatial consistency and compatibility between data layers; Overlay and cross-analyze multiple data sets to form a comprehensive spatial data set, supporting spatial visualization and multi-dimensional analysis; S35, regional map information overlay: overlay regional map information (such as administrative division map, road map, river map, etc.) with environmental data, human activity data and resource distribution data to enrich the context information of the spatial data set; Through GIS tools, administrative division information, transportation networks, geographic boundaries and other data are superimposed with other data layers to ensure the spatial relevance and geographic operability of the data; Use spatial analysis tools in geographic information systems, combined with regional maps, to conduct spatial analysis and extraction to ensure the spatial positioning accuracy of data; S36, data set generation: through S31 to S35, a geographic information data set is formed. The geographic information data set includes environmental data, human activity data, resource distribution data and regional map information. The data set supports spatial analysis, decision support systems and other advanced analysis tasks.

[0031] S4 includes: S41, Dynamic monitoring system architecture design: Build a dynamic monitoring system based on the GIS platform. The dynamic monitoring system obtains environmental data, human activity data and resource distribution data in real time, and combines geographic information data sets to monitor real-time resources and environmental factors; S42, Delineation of monitoring areas: In the GIS platform, the boundaries of the monitoring areas are delineated according to the actual survey area, and different monitoring key areas are divided according to resource types and human activity density, including: S421, delineate monitoring areas: In the GIS platform, by loading regional map information, multiple monitoring areas are delineated based on the distribution of natural resources and the density of human activities. The boundaries and monitoring focus of each area are divided according to resource types, environmental protection needs and survey objectives; For example, in areas with abundant water resources, the monitoring focus can be on changes in water quality and water consumption; while in areas with dense human activities, the monitoring focus can be on changes in traffic flow and pollution sources; S422, define sub-areas and monitoring priorities: Based on the survey needs, the large monitoring area can be subdivided into multiple sub-areas, and monitoring priorities can be set for each sub-area. For example, for forest resource areas, different forest type areas can be divided, and attention should be paid to the forest coverage and vegetation changes in each sub-area; for pollution source monitoring areas, they can be further subdivided into pollution source concentration change areas, traffic flow change areas, etc.

[0032] S423, Combining monitoring areas with spatial data: When defining the monitoring areas, combine existing spatial data (such as land use maps, regional development plans, etc.) to further optimize the boundaries of the monitoring areas. Ensure that the monitoring areas match the needs of resource protection or development.

[0033] S424, Dynamically adjust the monitoring area: As data collection continues, dynamically adjust the boundaries or monitoring focus of the monitoring area based on real-time data changes and analysis results. For example, if the concentration of a pollution source fluctuates dramatically, the monitoring scope of the area can be automatically expanded to ensure that potential environmental hazards are not missed; S43, real-time update of dynamic monitoring data: Based on the real-time collected environmental data, human activity data and resource distribution data, the dynamic monitoring system updates the monitoring data in real time and converts it into a visual display form, such as a heat map, a time series map, etc., including: Real-time data update mechanism: Based on the collected environmental data, human activity data and resource distribution data, the dynamic monitoring system continuously updates the monitoring data. These data updates can be set at fixed time intervals (such as every hour, every day) or trigger mechanisms (such as automatic updates when data changes are detected); Visual display of dynamic monitoring data: Generate visualization layers through the GIS platform and display them on the user interface. The visualization forms include resource distribution change heat map, environmental factor fluctuation map, time series change trend map, etc. For example, the spatial distribution of pollutant concentrations can be displayed through heat maps, or the monthly change trend of water resources in a certain area can be displayed through time series change maps. Users can conduct real-time queries through interactive maps to view monitoring data within a certain period of time.

[0034] S4 also includes: S44, construction of spatiotemporal analysis model: Based on GIS technology, a spatiotemporal analysis model is constructed to analyze the spatiotemporal relationship between resource changes and environmental factors. The spatiotemporal analysis model is combined with a spatial autoregressive algorithm to predict future resource change trends by superimposing historical data with real-time data; The spatial autoregression algorithm establishes the spatial dependency relationship between variables by considering the spatial correlation. The basic form of the spatiotemporal analysis model is: ; in, is the dependent variable (e.g., water resources, land use, etc.), is the spatial weight matrix, which represents the spatial dependence structure of the data. is the spatial autoregression coefficient, is the regression coefficient, are independent variables (e.g., environmental factors, climate data, etc.), As the error term, the model can capture the mutual influence of spatial locations and analyze spatiotemporal data to further predict the spatial distribution and change trend of resources; S45, mining of spatiotemporal change patterns: using spatiotemporal change analysis methods to identify the dynamic change patterns of resource distribution and environmental factors, and through spatiotemporal analysis models to identify the changing trends of resources (such as water resources, mineral resources, forest resources, etc.) in different seasons, climate conditions, or different regional backgrounds, and reveal the spatiotemporal correlation between resource fluctuations and environmental factors (such as precipitation, temperature, land use, etc.); For example, by analyzing the changing patterns of water resources in different seasons, identifying the distribution differences of water resources in the dry season and the rainy season, or identifying the spatiotemporal characteristics of the diffusion pattern of pollution sources, it helps decision makers grasp the dynamic changes of resources and provide data support for future resource scheduling and management.

[0035] S46, Dynamic Change Prediction: Based on historical data and real-time monitoring data, the future change trend of resources and environmental factors is predicted through regression analysis. The specific steps include: Establish trend models through historical data to analyze the cyclical, seasonal and long-term trends of resource changes.

[0036] Combined with real-time monitoring data, regression analysis methods (such as time series regression analysis, linear regression, nonlinear regression, etc.) are used to predict changes in resource and environmental factors in the future.

[0037] For example, the time series regression model can be used to predict future water reserves, pollutant concentrations, etc., thus providing a scientific basis for the formulation of resource management and environmental protection measures; The specific regression analysis formula is expressed as: ; in, Indicates time point The predicted values ​​(such as water resources reserves, pollutant concentrations, etc.), is the regression constant, is the regression coefficient, For time point independent variables (such as temperature, precipitation, etc.), is the error term. Through regression analysis of historical data and real-time data, future changes in resources and environment are predicted, providing data support for subsequent resource scheduling and management; S47, Dynamic monitoring data generation: Combined with the results of spatiotemporal change analysis, dynamic monitoring data is generated. The dynamic monitoring data includes the spatiotemporal distribution information of resources and the changes in environmental factors. The generated dynamic monitoring data can reflect the changing trends and spatial differences of resource distribution and environmental factors in real time. Through dynamic calculation and real-time updating, the timeliness and accuracy of monitoring data are ensured. This data can provide a basis for subsequent decision support and early warning systems.

[0038] S5 includes: S51, Model input data selection and processing: Select key factors affecting the ecological environment based on dynamic monitoring data. The key factors include: Environmental factor data (such as temperature, precipitation, soil moisture, etc.); Human activity data (such as land use change, pollution source distribution, etc.); Resource consumption data (such as the exploitation of water resources and mineral resources, etc.); S52, selection of environmental impact factors: according to actual needs and regional characteristics, different environmental impact factors (such as water quality, air quality, soil quality, etc.) are selected as evaluation variables, and dynamic monitoring data are combined with environmental factors to establish an ecological environmental impact assessment model; For example, water quality monitoring data can be combined with pollution source distribution data to assess the impact of pollution sources on water quality; Use vegetation cover combined with soil moisture data to assess the risk of land degradation; S53, Ecological and environmental impact assessment model formulation: Based on the ecological and environmental impact assessment model, the relationship between various environmental factors and ecological and environmental impacts is established, expressed as: ; in, is the ecological environment impact index, is the distribution of pollution sources, For water resource consumption, For soil degradation, are environmental factors (such as temperature, precipitation, etc.); S54, Grey correlation analysis method: When conducting ecological environmental impact assessment, the grey correlation analysis method is used to quantify the relationship between various environmental factors and ecological environmental impact. The specific steps are as follows: S541, calculate the grey correlation degree: based on the grey correlation analysis model, calculate the grey correlation degree between various environmental factors (such as pollution source distribution, climate change, etc.) and the impact on the ecological environment to determine the degree of impact between them; Grey relational formula: ; in, is the grey relational degree, For the The difference of the factors, is the difference between the reference sequence and is the resolution coefficient (usually 0.5); S542, determine the impact weight: according to the calculation result of grey correlation, determine the weight of each environmental factor on the ecological environment. The factor with a higher weight value represents a greater contribution to the environmental impact; The weighted average calculation formula of grey relational degree is expressed as: ; in, For the The weight of the environmental factors, The corresponding grey relational degree is: is the comprehensive impact index; S55, generation of assessment results: Based on the calculation results of grey correlation analysis, the ecological and environmental impact assessment results of different regions are output, and the environmental impact level (such as light pollution, moderate pollution, heavy pollution, etc.) is generated. Through these assessment results, the degree of environmental damage that may be caused in the process of resource utilization can be obtained, prompting managers to take corresponding environmental protection measures.

[0039] The resource utilization model includes a resource consumption sub-model and an environmental impact prediction sub-model, specifically including: S56, resource consumption sub-model construction: Based on dynamic monitoring data, collect and integrate the utilization data of different resources (such as water resources, mineral resources, forest resources, etc.), including the amount of resource extraction, use and recycling, and combine environmental factors (such as climate change, land use, etc.) to establish a resource consumption sub-model to predict the resource consumption trend in different regions, expressed as: ; in, For time point of resource stocks, is the initial resource stock, For time point The resource consumption, is the natural regeneration of resources, is the error term; S57, Construction of environmental impact prediction sub-model: Based on the resource consumption sub-model and environmental factor data, an environmental impact prediction sub-model is established to predict the potential impact of resource consumption on the ecological environment under different resource utilization conditions. For example, based on water resource consumption data, the risk of water pollution or soil erosion is predicted; Analyze the relationship between resource consumption and environmental factors (such as water quality changes, soil erosion, etc.), expressed as: ; in, For environmental impact, is the resource consumption, The climate change factor, for land use; S58, prediction of sustainable resource utilization: Based on the resource consumption sub-model and the environmental impact prediction sub-model, predict the sustainable utilization status of different regions and different resources, evaluate the sustainability of resources in a certain period of time in the future, the model outputs the sustainable utilization index of resources, and proposes resource utilization optimization plans based on the prediction results.

[0040] For example, based on water consumption data and predicted ecological impact results, the water resource sustainability of a certain area can be predicted and water-saving strategies or scheduling plans can be provided.

[0041] S6 includes: S61, Generate optimization plan based on ecological and environmental impact assessment results: Based on the assessment results of the ecological and environmental impact assessment model, analyze the environmental change trends in different regions and the potential impact of resource utilization on the environment, and generate natural resource optimization utilization plans for different regions, including: Assess regional environmental conditions: Based on the results of the ecological and environmental impact assessment model, identify potential risk areas for environmental damage (such as water pollution, soil degradation, etc.).

[0042] Optimize resource allocation: In areas with greater ecological and environmental risks, give priority to resource-saving development and optimize resource allocation to reduce negative impacts on the environment.

[0043] Propose protection measures: Propose targeted environmental protection measures for areas with greater environmental impact, such as strengthening pollution source management and implementing ecological restoration.

[0044] S62, generating environmental protection measures based on the prediction results of the resource utilization model: according to the prediction results of the resource utilization model, analyzing the sustainable utilization status and consumption trend of different resources, and generating corresponding environmental protection measures, specifically including: Predict resource consumption trends: Through resource utilization models, predict the consumption and consumption rate of different resources, and analyze the sustainability of resources in different time periods.

[0045] Propose resource protection measures: Based on resource consumption trends, propose corresponding resource protection measures, such as limiting mining, increasing recycling rates, etc.

[0046] Integration with environmental impact assessment: Based on the environmental impacts predicted by the resource consumption model (such as pollution, ecological damage, etc.), combined with environmental protection measures, formulate a comprehensive resource protection and environmental restoration plan.

[0047] S63, comprehensive generation of optimized utilization schemes and environmental protection measures: The optimized schemes and protection measures in S61 and S62 are integrated to form a comprehensive natural resource utilization and environmental protection optimization scheme, which comprehensively considers the balance between resource consumption and environmental impact, and aims to achieve sustainable utilization of resources and protection of the ecological environment, including: Scheme optimization: Optimize resource development and utilization schemes by integrating multiple factors such as ecological protection, resource utilization, and regional characteristics; Comprehensive report generation: A detailed plan report is generated, including the optimized resource utilization plan, environmental protection measures, specific implementation paths, monitoring mechanisms, etc.

[0048] like Figure 2 As shown, a GIS-based natural resource survey system is used to implement the above-mentioned GIS-based natural resource survey method, including the following modules: Data collection module: By deploying a variety of sensors, remote sensing satellites and drone equipment, it collects environmental data, human activity data and resource distribution data in the natural resource survey area in real time; Data preprocessing module: cleans and denoises the collected environmental data, human activity data, and resource distribution data, removes invalid data and outliers, and performs standardization processing; Spatial data integration module: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; Dynamic monitoring and analysis module: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change rules of resources and generate dynamic monitoring data; Resource utilization assessment module: Based on dynamic monitoring data, combined with the ecological environmental impact assessment model and resource utilization model, it analyzes the sustainable utilization status of different regions and different resources, and predicts the trend of resource consumption and environmental impact; Decision support module: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

[0049] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0050] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A natural resource survey method based on GIS, characterized in that: The following steps are involved: S1, data collection: by deploying a variety of sensors, remote sensing satellites and drone equipment, real-time collection of environmental data, human activity data and resource distribution data in the natural resource survey area; S2, data preprocessing: cleaning and denoising the collected environmental data, human activity data, and resource distribution data, removing invalid data and outliers, and performing standardization; S3, spatial data integration: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; S4, dynamic monitoring and analysis: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change laws of resources and generate dynamic monitoring data; S5, resource utilization assessment: Based on dynamic monitoring data, combined with ecological environmental impact assessment models and resource utilization models, analyze the sustainable utilization status of different regions and different resources, and predict the trend of resource consumption and environmental impact; S6, decision support: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

2. A GIS-based natural resource survey method according to claim 1, characterized in that: The S1 includes: S11, environmental data collection: by deploying a variety of sensors and equipment, real-time environmental data is collected in the natural resources survey area, the environmental data including meteorological data, soil moisture and vegetation coverage; S12, human activity data collection: by deploying a variety of monitoring equipment, real-time collection of human activity data in the natural resource survey area, the human activity data including land use type, pollution source distribution and traffic flow; S13, resource distribution data collection: by deploying remote sensing satellites and unmanned aerial vehicle equipment, real-time collection of natural resource distribution data, the resource distribution data includes water resources, mineral resources and forest resources.

3. A GIS-based natural resource survey method according to claim 2, characterized in that: The S2 includes: S21, data cleaning: data cleaning of collected environmental data, human activity data and resource distribution data; S22, data denoising: denoising the cleaned environmental data, human activity data and resource distribution data; S23, data standardization processing: standardize the denoised environmental data, human activity data and resource distribution data.

4. A GIS-based natural resource survey method according to claim 3, characterized in that: The S3 includes: S31, spatial calibration of environmental data: matching environmental data with a geographic coordinate system; S32, spatial calibration of human activity data: aligning human activity data with geographic coordinates; S33, spatial calibration of resource distribution data: calibrating the spatial coordinates of resource distribution data; S34, Data integration based on GIS platform: Use GIS platform to spatially integrate data from different sources; S35, regional map information superposition: superimposing the regional map information with environmental data, human activity data and resource distribution data; S36, data set generation: through S31 to S35, a geographic information data set is formed, and the geographic information data set includes environmental data, human activity data, resource distribution data and regional map information.

5. A GIS-based natural resource survey method according to claim 4, characterized in that: The S4 includes: S41, Dynamic monitoring system architecture design: Build a dynamic monitoring system based on the GIS platform. The dynamic monitoring system obtains environmental data, human activity data and resource distribution data in real time, and combines geographic information data sets to monitor real-time resources and environmental factors; S42, monitoring area delineation: In the GIS platform, the boundaries of the monitoring area are delineated according to the actual survey area, and different monitoring areas are divided according to resource types and human activity density; S43, real-time update of dynamic monitoring data: Based on the real-time collected environmental data, human activity data and resource distribution data, the dynamic monitoring system updates the monitoring data in real time and converts it into a visual display form.

6. A GIS-based natural resource survey method according to claim 5, characterized in that: The S4 further comprises: S44, construction of spatiotemporal analysis model: Based on GIS technology, a spatiotemporal analysis model is constructed to analyze the spatiotemporal relationship between resource changes and environmental factors. The spatiotemporal analysis model is combined with a spatial autoregressive algorithm to predict future resource change trends; S45, mining of spatiotemporal change patterns: using spatiotemporal change analysis methods to identify the dynamic change patterns of resource distribution and environmental factors; S46, Dynamic Change Prediction: Based on historical data and real-time monitoring data, the future change trend of resources and environmental factors is predicted through regression analysis; S47, dynamic monitoring data generation: Combined with the results of spatiotemporal change analysis, dynamic monitoring data is generated. The dynamic monitoring data includes the spatiotemporal distribution information of resources and the changes in environmental factors.

7. A GIS-based natural resource survey method according to claim 6, characterized in that: The S5 includes: S51, model input data selection and processing: select key factors affecting the ecological environment based on dynamic monitoring data; S52, selection of environmental impact factors: according to actual needs and regional characteristics, different environmental impact factors are selected as evaluation variables, and dynamic monitoring data are combined with environmental factors to establish an ecological environmental impact assessment model; S53, Ecological and environmental impact assessment model formulation: Based on the ecological and environmental impact assessment model, establish the relationship between various environmental factors and ecological and environmental impacts; S54, Grey correlation analysis method: When conducting ecological environmental impact assessment, the grey correlation analysis method is used to quantify the relationship between various environmental factors and ecological environmental impact; S55, generation of assessment results: based on the calculation results of grey correlation analysis, output the ecological environmental impact assessment results of different regions and generate environmental impact grades.

8. A GIS-based natural resource survey method according to claim 7, characterized in that: The resource utilization model includes a resource consumption sub-model and an environmental impact prediction sub-model, specifically including: S56, resource consumption sub-model construction: Based on dynamic monitoring data, collect and integrate the utilization data of different resources, including the amount of resource extraction, use and recycling, and combine environmental factors to establish a resource consumption sub-model to predict the resource consumption trend in different regions; S57, Construction of environmental impact prediction sub-model: Based on the resource consumption sub-model and environmental factor data, an environmental impact prediction sub-model is established to predict the potential impact of resource consumption on the ecological environment under different resource utilization conditions; S58, prediction of sustainable resource utilization: Based on the resource consumption sub-model and the environmental impact prediction sub-model, predict the sustainable utilization status of different regions and different resources, and evaluate the sustainability of resources in a certain period of time in the future.

9. A GIS-based natural resource survey method according to claim 8, characterized in that: The S6 includes: S61, Generate optimization plan based on ecological and environmental impact assessment results: Based on the assessment results of the ecological and environmental impact assessment model, analyze the environmental change trends in different regions and the potential impact of resource utilization on the environment, and generate natural resource optimization utilization plans for different regions; S62, generating environmental protection measures based on the prediction results of the resource utilization model: analyzing the sustainable utilization status and consumption trends of different resources according to the prediction results of the resource utilization model, and generating corresponding environmental protection measures; S63, comprehensive generation of optimized utilization plans and environmental protection measures: Integrate the optimization plans and protection measures in S61 and S62 to form a comprehensive natural resource utilization and environmental protection optimization plan.

10. A GIS-based natural resource survey system, used to implement a GIS-based natural resource survey method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection module: By deploying a variety of sensors, remote sensing satellites and drone equipment, it collects environmental data, human activity data and resource distribution data in the natural resource survey area in real time; Data preprocessing module: cleans and denoises the collected environmental data, human activity data, and resource distribution data, removes invalid data and outliers, and performs standardization processing; Spatial data integration module: Use the GIS platform to spatially integrate data from different sources, calibrate environmental data, human activity data, and resource distribution data according to spatial coordinates, and combine them with regional map information to form a geographic information data set; Dynamic monitoring and analysis module: Based on the real-time collected environmental data, human activity data and resource distribution data, combined with geographic information data sets, a dynamic monitoring system is established through GIS technology to monitor resource changes and fluctuations of environmental factors in real time, and combined with spatiotemporal change analysis to identify the dynamic change rules of resources and generate dynamic monitoring data; Resource utilization assessment module: Based on dynamic monitoring data, combined with the ecological environmental impact assessment model and resource utilization model, it analyzes the sustainable utilization status of different regions and different resources, and predicts the trend of resource consumption and environmental impact; Decision support module: Generate optimal utilization plans for natural resources and environmental protection measures based on the assessment results of the ecological and environmental impact assessment model and the prediction results of the resource utilization model.

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